title: Monte Carlo SURE-based parameter selection for parallel magnetic resonance imaging reconstruction.
abstract: OBJECTIVE
  Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques.
  
  
  BACKGROUND
  We derive a weighted MSE criterion appropriate for data-preserving regularized parallel imaging reconstruction and the corresponding weighted Stein's unbiased risk estimate. We describe a Monte Carlo approximation of the weighted Stein's unbiased risk estimate that uses two evaluations of the reconstruction method per candidate parameter value.
  
  
  METHODS
  We reconstruct images using the denoising sparse images from GRAPPA using the nullspace method (DESIGN) and L1 iterative self-consistent parallel imaging (L1 -SPIRiT). We validate Monte Carlo Stein's unbiased risk estimate against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE-optimal parameters.
  
  
  RESULTS
  Our method selects nearly MSE-optimal regularization parameters for both DESIGN and L1 -SPIRiT over a range of noise levels and undersampling factors.
  
  
  CONCLUSIONS
  The proposed method automatically provides nearly MSE-optimal choices of regularization parameters for data-preserving nonlinear parallel MRI reconstruction methods.
authors.affiliation: Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Weller
authors.forename: Sathish
authors.initials: S
authors.lastname: Ramani
authors.forename: Jon-Fredrik
authors.initials: JF
authors.lastname: Nielsen
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Fessler
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Monte Carlo methods; Stein's unbiased risk estimate; parallel imaging reconstruction; regularization parameter selection